Predicting the impact of a personnel action on a worker

ABSTRACT

A computer-implemented method for predicting the impact of a personnel action on a worker is provided. The method includes collecting a plurality of attributes associated with each of a plurality of workers, receiving a proposed personnel action related to a worker, applying a data mining tool to the attributes and the proposed personnel action to identify an impact of the proposed personnel action, and outputting the impact of the proposed personnel action based on the result produced by the data mining tool.

BACKGROUND

1. Field

Embodiments of the invention are generally related to computer systemsand, in particular, human resource or human capital management systems.

2. Description of the Related Art

Human resource management generally refers to the strategic processesorganizations use to manage people. Organizations utilize human resourcemanagement processes to attract appropriately skilled employees,integrate them into the organization, assess and develop theircompetencies, and retain their commitment. In order to achieve thesegoals, companies may implement several processes including workforceplanning, recruitment, orientation, skills management and training,salary compensation and benefits administration, and performanceappraisal. Therefore, the human resources management function of anorganization includes a variety of activities, such as deciding staffingneeds and determining how to fulfill them, recruiting and training thebest employees, ensuring they are and continue to be high performers,addressing performance issues, developing and managing an approach toemployee benefits and compensation, and ensuring that personnel andmanagement practices conform to various regulations.

Given the breadth and complexity of human resource management functions,companies utilize information technology systems and/or softwareapplications to help manage and streamline the process. Theseapplications allow enterprises to automate many aspects of humanresource (HR) management, with the dual benefits of reducing theworkload of the HR department as well as increasing the efficiency ofthe department by standardizing HR processes. An example of such a humanresource management application is the Human Capital Management (HCM)Fusion® application from Oracle® Corporation.

SUMMARY

According to one embodiment, a computer-implemented method forpredicting the impact of a personnel action on an employee is provided.The method includes collecting a plurality of attributes associated witheach of a plurality of workers, receiving a proposed personnel actionrelated to a worker, applying a data mining tool to the attributes andthe proposed personnel action to identify an impact of the proposedpersonnel action, and outputting the impact of the proposed personnelaction based on the result produced by the data mining tool.

BRIEF DESCRIPTION OF THE DRAWINGS

For proper understanding of the invention, reference should be made tothe accompanying drawings, wherein:

FIG. 1 illustrates a block diagram of a system according to oneembodiment of the invention;

FIG. 2 illustrates a flow chart of a method according to one embodiment;

FIG. 3 illustrates a flow chart of a method according to anotherembodiment;

FIG. 4 illustrates a user interface according to one embodiment;

FIG. 5 a illustrates a user interface according to an embodiment;

FIG. 5 b illustrates a user interface according to another embodiment;

FIG. 6 illustrates a user interface according to an embodiment;

FIG. 7 illustrates a user interface according to an embodiment;

FIG. 8 a illustrates a user interface according to an embodiment;

FIG. 8 b illustrates a user interface according to another embodiment;

FIG. 9 illustrates a user interface according to an embodiment;

FIG. 10 illustrates a user interface according to an embodiment;

FIG. 11 illustrates a user interface according to an embodiment;

FIG. 12 illustrates a user interface according to one embodiment;

FIG. 13 a illustrates a user interface according to an embodiment; and

FIG. 13 b illustrates a user interface according to another embodiment.

DETAILED DESCRIPTION

Many employers and organizations face issues with top performingemployees leaving to join competitors without warning. Organizations mayalso face a similar problem with the performance of employeesdiminishing thereby resulting in a loss of productivity. Both of theseissues may result in a high cost of replacement of employees in terms oftime and money. Therefore, embodiments of the invention provide a systemwhich can apply advanced statistical methods and data mining to predictthe chance of future attrition and the potential of an individualassociated with the organization or for a group of employees.

More specifically, one embodiment is directed to a system for predictingfuture performance and/or the likelihood of attrition for a worker. Theworker may be an employee, contingent worker, contractor, or anyindividual associated with an organization. The system is configured tocollect attributes associated with the workers in the organization. Theattributes may be related to the worker's background, as well as theirjob responsibilities, past performance, compensation, and any otherrelevant attributes. The system is then configured to apply a datamining model to those attributes. The data mining model analyzes theattributes as they relate to all workers and identifies a patternbetween the attributes and the future performance of the workers ortheir likelihood of attrition. The system is further configured to usethe identified pattern to predict future performance or likelihood ofattrition for a specific worker. In an embodiment, the system isincluded within a human resource management application, such as theHuman Capital Management (HCM) Fusion® application from Oracle®Corporation.

In one example, the system is able to mine existing worker data usingdata mining tools in order to predict a worker's risk of leaving and thefuture performance levels of the current workforce. Additionally, thesystem, via the data mining tool, can identify the top reasons thatcontribute positively or negatively in deriving the prediction value. Inother words, the system can identify the specific attributes that mostcontribute to the prediction.

As a result, when retention and performance issues arise with respect tocertain employees, the organization's human resource system is able topredict, forewarn, and help managers to take corrective actions toimprove organizational stability and thereby increase overallproductivity. By providing such predictions, embodiments of theinvention will result in a substantial cost savings in terms ofreplacement cost and time needed to hire a comparable replacementworker, as well as the time required to bring that new worker up tospeed and producing at the desired level of productivity.

Additionally, other embodiments of the invention provide a system foridentifying and predicting the impact of a personnel action on anindividual worker or employee and their peers. For example, the systemmay provide a manager with a prediction of a result of some action, suchas a promotion or salary increase, before such action is taken. Thus,the system will provide valuable insight into the decision makingprocess for a personnel action and how it may affect retention andperformance of the worker and their peers.

FIG. 1 illustrates a block diagram of a system 10 that may implement oneembodiment of the invention. System 10 includes a bus 12 or othercommunications mechanism for communicating information betweencomponents of system 10. System 10 also includes a processor 22, coupledto bus 12, for processing information and executing instructions oroperations. Processor 22 may be any type of general or specific purposeprocessor. System 10 further includes a memory 14 for storinginformation and instructions to be executed by processor 22. Memory 14can be comprised of any combination of random access memory (“RAM”),read only memory (“ROM”), static storage such as a magnetic or opticaldisk, or any other type of machine or computer readable media. System 10further includes a communication device 20, such as a network interfacecard or other communications interface, to provide access to a network.As a result, a user may interface with system 10 directly or remotelythrough a network or any other method.

Computer readable media may be any available media that can be accessedby processor 22 and includes both volatile and nonvolatile media,removable and non-removable media, and communication media.Communication media may include computer readable instructions, datastructures, program modules or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media.

Processor 22 is further coupled via bus 12 to a display 24, such as aLiquid Crystal Display (“LCD”), for displaying information to a user,such as configuration information. A keyboard 26 and a cursor controldevice 28, such as a computer mouse, are further coupled to bus 12 toenable a user to interface with system 10. Processor 22 and memory 14may also be coupled via bus 12 to a database system 30 and, thus, may beable to access and retrieve information stored in database system 30.Although only a single database is illustrated in FIG. 1, any number ofdatabases may be used in accordance with certain embodiments. In someembodiments, database system 30 may store employee data, such as jobresponsibilities, past performance, compensation, and any other relevantattributes.

In one embodiment, memory 14 stores software modules that providefunctionality when executed by processor 22. The modules may include anoperating system 15 that provides operating system functionality forsystem 10. The memory may also store a predictive analytic module 16,which provides a prediction of the future performance and/or likelihoodof attrition of a worker or employee.

In one embodiment, predictive analytic module 16 is configured tocollect and analyze attributes associated with company employees. Theattributes may be related to an employee's background, their jobresponsibilities, past performance, compensation, and any other relevantattributes. Predictive analytic module 16 applies a data mining model tothose attributes. The data mining model analyzes the attributes as theyrelate to all employees and identifies a pattern between the attributesand the future performance of the employees or their likelihood ofattrition. Predictive analytic module 16 is further configured to usethe identified pattern to the predict future performance or likelihoodof attrition for a specific employee. In addition, predictive analyticmodule 16 can be configurable by users to take into account additionalattributes, to remove existing attributes, or to weight certainattributes differently.

In other embodiments, predictive analytic module 16 can provide apredicted result of some personnel action on the employee and theirpeers. For instance, a manager may be considering providing an employeewith a salary increase or promotion. Prior to finalizing such an action,the manager may input the contemplated action into system 10, andpredictive analytic module 16 will provide a prediction of the likelyresult of the action. As a result, the manager will have more completeand tangible information regarding the result of the action on theemployee and their peers thereby allowing the manager to make a moreinformed decision.

System 10 may also include one or more other functional modules 18 toprovide additional functionality. For example, functional modules 18 mayinclude a human capital management application module or any modulesrelated to an enterprise human resource system.

Database system 30 may include a database server and any type ofdatabase, such as a relational or flat file database. Database system 30may store attributes related to employees including their background,responsibilities, performance and compensation. Database system 30 mayalso store any other data required by the predictive analytic module 16,or data associated with system 10 and its associated modules andcomponents.

In certain embodiments, processor 22, predictive analytic module 16, andother functional modules 18 may be implemented as separate physical andlogical units or may be implemented in a single physical and logicalunit. Furthermore, in some embodiments, processor 22, predictiveanalytic module 16, and other functional modules 18 may be implementedin hardware, or as any suitable combination of hardware and software.

As mentioned above, embodiments of the invention utilize a number ofemployee attributes to produce a prediction of future employeeperformance and/or future likelihood of attrition. The attributes can beconfigured by users according to their requirements, including removingor adding certain attributes from the analysis. Table 1 illustratesexamples of some of the attributes that may be used to compile thepredictions.

TABLE 1 Attribute Description Amount of leave in the previous yearNumber of days leave in the previous year Amount of sickness Number ofdays sickness in the current year Amount of sickness in the previousNumber of days sickness in the previous year year Appraising managerAppraising manager Average salary change Average % change Currentassignment status Assignment status Current business group Businessgroup Current department Department Current grade Grade Current job JobCurrent location Location Current manager Manager Current managerCurrent manager Current position Position Expected working hoursExpected weekly working hours FTE (full time equivalent) FTE value Has asecond passport Home city Home city Home country Home country Increasein sickness over previous year Legal entity Legal entity LegislationLegislation Length of service Length of service in years Length of timesince last received Length of time since received options in optionsmonths Length of time since last salary Length of time since last salarychange in change months Manager's performance Manager's overallperformance rating Most recent salary change % change in salary for themost recent change Nationality Nationality Normal working end timeNormal working end time Normal working start time Normal working starttime Number of days leave taken while at Number of days leave thisenterprise Number of different departments Count of the number ofdifferent worked in departments Number of different grades Count of thenumber of different grades Number of grade changes in the last 2 Countof the number of grade changes in the years last 2 calendar years Numberof managers in the last 5 Count of the number of different manager inyears the last 5 calendar years Number of sicknesses in the previousNumber of distinct sicknesses in the year previous year (a sickness of10 days is counted as 1) Number of stock options compared to Ratio ofnumber of stock options held vs the others on the same grade averagenumber of stock options of others on the same grade Performance haschanged Potential profit on stock Potential profit on stock expressed inusers currency Previous performance Previous overall performance ratingPreviously employed at the enterprise Ratio of vested vs unvestedoptions Ratio of vested vs unvested options Reason for last salarychange Salary change reason Tabacco user Time in current grade Number ofmonths in current grade Time in current job Number of months in currentjob Time since last leave Number of months since last leave absence Timesince last probation ended Number of months since probation ended Timesince last sickness Number of months since last sickness absence Timesince the marital status last Number of years since marital status lastchanged change Time spent in current department Number of months incurrent department Time spent in current position Number of months incurrent position Time spent in each department Average number of monthsspent in each department Time spent in each grade Average number ofmonths spent in each grade Time spent in each job Average number ofmonths spent in each job Time spent in each position Average number ofmonths spent in each position Time spent with each manager Averagenumber of months spent in each manager Time spent with the currentmanager Number of months with current manager Time to end of contractNumber of months until the end of contract Time until next salary reviewNumber of months until next salary review Time until the nextperformance Number of months until next performance review review Visaexpiration Number of weeks until visa expires Willing to relocatedomestically Worker category Worker category Worker is an employeeWorker is willing to relocate internationally Worker's currentperformance rating Current overall performance rating Worker's currentself performance Current self assessed performance rating ratingWorker's performance assessment differs from managers Worker'sperformance compared to Ratio of workers overall performance ratingpeers vs the average for peers (i.e. others reporting directly to thesame manager) Worker's stock options compared to Ratio of the number ofstock options vs the peers average for peers (i.e. others reportingdirectly to the same manager)

FIG. 2 illustrates a flow diagram of a method according to oneembodiment. In some embodiments, the flow diagram of FIG. 2 shows someof the functionality of predictive analytic module 16. In oneembodiment, the functionality of the flow diagram of FIG. 2, and FIG. 3below, is implemented by software stored in memory or other computerreadable or tangible media, and executed by a processor. In otherembodiments, the functionality may be performed by hardware (e.g.through the use of an application specific integrated circuit (“ASIC”),a programmable gate array (“PGA”), a field programmable gate array(“FPGA”), or any combination of hardware and software.

At 200, a plurality of attributes related to employees of the companyare collected. As mentioned above, these attributes may be related to anemployee's background, their job responsibilities, past performance,compensation, or any other relevant attributes. At 210, a data miningtool is applied to the attributes in order to identify a pattern betweenthe attributes and the future performance of the employees, or a patternbetween the attributes and the future likelihood of attrition of theemployees. In some embodiments, when predicting attrition, the datamining tool is controlled, for example by predictive analytic module 16,to identify patterns that resulted in a voluntary termination. Thus, inthis case, the data mining tool looks for past cases where the workerwas terminated and the termination was of a voluntary nature. The datamining tool can then analyze these cases to find patterns betweenemployee attributes and attrition. Embodiments of the invention can thenapply these patterns to current workers to predict their likelihood ofattrition.

In other embodiments, when predicting future performance, the datamining tool is controlled to identify patterns that result in theworker's overall performance rating. For example, the data mining toolmay identify patterns that are typical for low performing workers andpatterns that are typical for high performing workers. Therefore, givena certain target attribute, such as voluntary attrition or highperformance, the data mining tool can identify the patterns thatresulted in that target attribute.

Then, at 220, the identified pattern is used to predict the futureperformance or likelihood of attrition of a specific employee. Accordingto one example, the method further includes, at 230, providing theprediction of the future performance or likelihood of attrition of theemployee to a user of system 10, such as a human resources manager. Theprediction may be provided to the user via a graphical user interface,such as a table or graph.

In this embodiment, data mining is used as a tool in the process ofpredicting a future characteristic of an employee, such as their futureperformance or their likelihood of leaving the organization. In general,data mining refers to the process of extracting patterns from data. Twocommonly used data mining techniques are classification and regression.The classification technique arranges the data into predefined groupsand is therefore the most commonly used technique for predicting aspecific outcome such as yes/no, high/medium/low-value, etc. Someclassification algorithms include Naive Bayes, Decision Tree, LogisticRegression, and Support Vector Machine (“SVM”).

The regression technique attempts to find a function which models thedata with the least error. Accordingly, regression is a technique forpredicting a continuous numerical outcome such as customer lifetimevalue, house value, process yield rates, etc. Some regression algorithmsinclude Multiple Regression and Support Vector Machine (“SVM”).

One embodiment of the invention provides at least two predictions: apredicted risk of leaving (attrition), and a predicted performance. Thepredicted risk of leaving predicts who is going to leave based on thedistribution of attributes of ex-employees and current employees. Thisprediction utilizes most or all of the attributes outlined in Table 1.Additionally, according to one embodiment, the risk of leaving ispredicted using a classification technique such as Generalized LinearModeling (“GLM”).

Generalized Linear Models (“GLMs”) include and extend the class oflinear models provided by Linear Regression. Linear models make a set ofrestrictive assumptions, most importantly, that the target (dependentvariable y) is normally distributed conditioned on the value ofpredictors with a constant variance regardless of the predicted responsevalue. An advantage of linear models and their restrictions includecomputational simplicity, an interpretable model form, and the abilityto compute certain diagnostic information about the quality of the fit.

GLMs relax these restrictions, which are often violated in practice. Forexample, binary (yes/no or 0/1) responses do not have the same varianceacross classes. Furthermore, the sum of terms in a linear model cantypically have very large ranges encompassing very negative and verypositive values. For the binary response example, it is preferred thatthe response is a probability in the range [0,1].

GLMs accommodate responses that violate the linear model assumptionsthrough two mechanisms: a link function and a variance function. Thelink function transforms the target range to potentially −infinity to+infinity so that the simple form of linear models can be maintained.The variance function expresses the variance as a function of thepredicted response, thereby accommodating responses with non-constantvariances (such as the binary responses).

Two of the most popular members of the GLM family of models (with theirmost popular link and variance functions) include: linear regressionwith the identity link and variance function equal to the constant 1(constant variance over the range of response values); and logisticregression with the logit link and binomial variance functions.

GLM is a parametric modeling technique. Parametric models makeassumptions about the distribution of the data. When the assumptions aremet, parametric models can be more efficient than non-parametric models.

The predicted performance of an employee predicts a future value of aworker based on their actual performance as well as all the otherattributes outlined in Table 1. In one embodiment, the futureperformance or value of an employee is predicted using a regressiontechnique such as Support Vector Machine (“SVM”).

SVM is a powerful, state-of-the-art algorithm with strong theoreticalfoundations based on the Vapnik-Chervonenkis theory. SVM has strongregularization properties. Regularization refers to the generalizationof the model to new data.

SVM models have similar functional form to neural networks and radialbasis functions, which are both popular data mining techniques. However,neural networks and radial basis algorithms do not have the well-foundedtheoretical approach to regularization that forms the basis of SVM. Thequality of generalization and ease of training of SVM is beyond thecapacities of these more traditional methods. SVM can model complex,real-world problems such as text and image classification, hand-writingrecognition, and bioinformatics and biosequence analysis.

SVM performs well on data sets that have many attributes, even if thereare very few cases on which to train the model. There is no upper limiton the number of attributes; the only constraints are those imposed byhardware. Traditional neural networks, on the other hand, do not performwell under these circumstances.

FIG. 3 illustrates a flow diagram of a method according to anotherembodiment. The flow diagram of FIG. 3 shows some of the functionalityof predictive analytic module 16. More specifically, FIG. 3 illustratesa method of predicting a result of a personnel action on an employee andtheir group prior to taking that action.

At 300, a plurality of attributes related to employees of the companyare collected. These attributes include at least the attributes listedin Table 1. At 310, a proposed personnel action is received. Theproposed personnel action can be, for example, a salaryincrease/decrease or a promotion/demotion. At 320, a data mining tool isapplied to the attributes and the proposed personnel action in order toidentify an impact of the proposed personnel action on the performanceof the employee and/or their peers. Then, at 330, the method includesoutputting the impact of the proposed personnel action based on theresult produced by the data mining tool. In one example, the predictedimpact is provided to the user via a graphical user interface, such as atable or graph.

FIG. 4 illustrates an example of an organizational summary userinterface 400 which can show the predicted attrition for employees ofthe organization. Organizational summary user interface 400 includes apage or table that lists the employee's name, job title, worker type,assignment type, telephone number, mobile telephone number, e-mail,local time, and identification number. In addition, organizationalsummary user interface 400 includes a predicted attrition section 410that shows the likelihood of an employee leaving. Predicted attritionsection 410 includes an “Individual” column which shows the predictedattrition for each of the individual workers as high, medium or low.Predicted attrition section 410 also includes a “Group” column thatshows the average predicted attrition for everyone in that worker'steam.

FIG. 5 a illustrates an example of a predicted worker performance andattrition user interface 500 that shows the predicted performance andattrition for each worker in a team or group. Predicted workerperformance and attrition user interface 500 includes a chart view 510which graphically represents the predicted attrition and predictedperformance for each worker in an XY chart. The y-axis of chart view 510shows the predicted attrition, while the x-axis shows the predictedperformance. Predicted worker performance and attrition user interface500 also includes a table view 510 that lists each of the workers, theiraverage predicted attrition level, and their average predictedperformance level in a table format. Predicted worker performance andattrition user interface 500 allows managers to easily identify thoseemployees or teams that are predicted high performers and are alsopredicted to be at a high risk of loss. As a result, managers are ableto take necessary steps to retain those employees or groups before theymake a decision to leave.

FIG. 5 b illustrates another example of a predicted worker performanceand attrition user interface 501 that shows the predicted performanceand attrition for a team or group of workers. Predicted workerperformance and attrition user interface 501 includes a chart view 530which graphically represents the predicted attrition and predictedperformance for each team in an XY chart. Similar to FIG. 5 a, they-axis of chart view 530 shows the predicted attrition, while the x-axisshows the predicted performance. Predicted worker performance andattrition user interface 501 also includes a table view 540 that liststhe team name, the total number of team members, the average probabilityof attrition, and the average predicted performance level in a tableformat. In some embodiments, a team may include only one worker.

FIG. 6 illustrates another example of a predicted worker performance andattrition user interface 600 with additional information. In thisexample, chart view 610 shows the names of the workers under theirrepresentation and illustrates a prediction of their performance andlikelihood of attrition based on the their representation's position onthe graph. For instance, a worker placed in the right, bottom square onthe graph has high predicted performance and low likelihood ofattrition; while a worker in the center square of the graph would havemedium predicted performance and a medium likelihood of attrition. Tableview 620 lists the individual or team name, actual performance ratingfor the individual or team, the probability of attrition, the predictedattrition reason, the predicted performance level, and an icon forobtaining additional prediction details. In some embodiments, a filterarea 630 is provided for filtering the results based, for example, onworker or team level, manager, jobs, grades, locations, predictedattrition, and predicted performance, as shown in

FIG. 7 illustrates a more detailed representation of chart view 510,530, or 610, for example. As shown in FIG. 7, a window 700 withadditional detail is shown, for example, when a cursor is hovered over aworker's depiction in chart view 510, 530, or 610. Window 700 mayinclude information such as the worker's name, the worker's averagepredicted performance as a percentage, and the worker's averagepredicted attrition as a percentage.

FIG. 8 a illustrates an example of a pop-up dialogue box 800 that showsdetails related to an individual worker. In one embodiment, pop-updialogue box 800 shows additional details related to the predictedattrition and predicted performance. For example, pop-up dialogue box800 may list the worker's name, position, manager, location, predictedperformance, current performance rating, predicted attrition, and riskof loss. Pop-up dialogue box 800 may also include a table 810 thatillustrates contributing factors for the predicted attrition orperformance, the current value of that factor, and the level ofcontribution of that factor (whether negative or positive) to thepredicted attrition or performance.

FIG. 8 b illustrates another example of a pop-up dialogue box 801 thatshows details related to a worker or team. In this example, pop-updialogue box 801 shows the details related to a team including the teammanager, average predicted performance, average predicted attrition, andthe total number of workers in the team. Pop-up dialogue box 801 mayalso include a graph that illustrates the topmost positive contributingfactors to attrition and/or the topmost positive contributing factors toperformance. In other embodiments, pop-up dialogue box 801 mayillustrate a graph of the topmost negative contributing factors toattrition and/or the topmost negative contributing factors toperformance.

FIG. 9 illustrates an example of a predicted work area user interface900 that a manager can use to further investigate a predictionassociated with a worker or team. In one embodiment, different teams areidentified with different colors. It is possible to filter out someteams and to highlight managers. In addition, it is possible to takesome proposed action, such as promote or transfer, by selecting one ofthe buttons in action area 910.

FIG. 10 illustrates an example of a predictive analytic dashboard userinterface 1000 that displays the results of a what-if analysis based onproposed actions, such as providing a promotion or pay increase. Thepredictive analytic dashboard user interface 1000 allows a user toexplore ways of changing a worker's predicted attrition and performancewithout actually taking any action. The chart of predictive analyticdashboard user interface 1000 can display the old prediction calculatedby system 10, and also display the new prediction based on the proposedaction. In other words, a manager can use predictive analytic dashboarduser interface 1000 to enter a proposed personnel action, and theresults of that proposed action will be displayed.

In some embodiments, the what-if column of table view 1020 lists anyattributes involved in the prediction that a manager or user may want tochange. These listed attributes may include some attributes that, inreality, a user cannot alter, such as length of service. However, theuser might still be interested to see whether changing such an attributewill have a positive or negative effect on the worker.

For example, a manager can change a value in the what-if column of tableview 1020 of predictive analytic dashboard user interface 1000 in orderto view how that change will effect attrition and performance. In oneembodiment, chart view 1010 will graphically display how such a changewill effect performance and/or attrition of the employee. Additionally,predictive analytic dashboard user interface 1000 can display the effectof any action on other members of team so that the manager can see thewider effects of any change.

The contribution column of table view 1020 indicates an attributes levelof contribution to the likelihood of attrition and future performance.In this example, the attributes in the what-if column are listed indescending order of the percentage contribution to the probability ofattrition, but this order may be changed by the user. The user canchange any of the attributes in the what-if column and see the effect onthe predictions, both in the table view 1020 and on chart view 1010. Inone embodiment, the contributions columns in the table will not changeas the user changes values in the what-if column.

Once the manager is pleased with the actions they have proposed,predictive analytic dashboard user interface 1000 can generate a list ofthe actions the manager specified during the what-if analysis, or itwill allow the manager to initiate an action. In another embodiment,system 10 can calculate the optimum actions to be taken by automaticallychanging what-if values until the optimum desired result is achieved.According to certain embodiments, the user decides which attributes areto be included in the calculation by system 10 and whether anyconstraints are to placed on those attributes. For instance, a usermight specify a constraint that the salary can only vary between −5% to10% of the worker's current salary.

FIG. 11 illustrates an example of a probability of attrition graph 1100according to one embodiment. According to this embodiment, the managercan view the worker's predicted attrition and performance both beforeand after the proposed action. For example, each time the managerchanges an attribute, such as a grade change or salary change, theprobability of attrition graph 1100 is updated to show the effect of thechange. Similar to FIG. 5, the y-axis of the probability of attritiongraph 1100 shows the predicted attrition, while the x-axis shows thepredicted performance. In addition, the effect of an action on the restof the team may also be illustrated by the probability of attritiongraph 1100.

FIG. 12 illustrates a what-if prediction action plan user interface 1200according to one embodiment. What-if prediction action plan userinterface 1200 can include a table that lists a current and proposedattribute for a worker. In this example, the current column of table1210 lists the worker's current position, grade, and recent salarychange. What-if prediction action plan user interface 1200 shows thecurrent predicted performance and current predicted attrition based onthe worker's current attributes. The what-if value column of table 1210lists proposed attributes for a worker, which may include a promotionand larger salary change. What-if prediction action plan user interface1200 also shows the new predicted performance and new predictedattrition based on the proposed attributes. According to this example, auser can see that the proposed attributes will result in a slightincrease in performance and a large reduction in the likelihood ofattrition.

Similarly, table 1220 of what-if prediction action plan user interface1200 shows the current working hours for a worker, and their currentperformance and current attrition values. Table 1220 also shows proposedworking hours, and new performance and new attrition values based onthat proposed change. Tables 1210 and 1220 also include a take actioncolumn that allows a user to click the icon shown in that column toexecute the proposed action.

FIG. 13 a illustrates a predictive model user interface 1300 accordingto one embodiment. Predictive model user interface 1300 shows thefactors contributing to attrition in a bar graph 1310. Bar graph 1310lists the contributing factors on the x-axis and the number of workersaffected on the y-axis. As a result, bar graph 1310 shows the number ofworkers affected by each contributing factor to attrition. Asillustrated in FIG. 13 b, predictive model user interface 1300 can alsoshow each factor's average percent contribution to attrition in graph1320.

In view of the above, embodiments of the invention provide a usefulsystem for predicting both the likelihood that an employee leaves acompany and their future performance. In one example, the systemutilizes data mining tools to analyze employee attributes to determine alink between those attributes and some future characteristic of theemployees. The system then uses the results of that data mining analysisto predict whether a specific employee is likely to leave as well astheir likely future performance. Other embodiments of the system allowmanagers to predict the results of a personnel action on an employee orgroup prior to officially taking that action.

It should be noted that many of the functional features described inthis specification have been presented as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom VLSIcircuits or gate arrays, off-the-shelf semiconductors such as logicchips, transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike.

Modules may also be partially implemented in software for execution byvarious types of processors. An identified module of executable codemay, for instance, comprise one or more physical or logical blocks ofcomputer instructions which may, for instance, be organized as anobject, procedure, or function. Nevertheless, the executables of anidentified module need not be physically located together, but maycomprise disparate instructions stored in different locations which,when joined logically together, comprise the module and achieve itsstated purpose.

Indeed, a module of executable code could be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.In order to determine the metes and bounds of the invention, therefore,reference should be made to the appended claims.

1. A computer-readable media having instructions stored thereon that,when executed by a processor, causes the processor to execute theinstructions comprising: collecting a plurality of attributes associatedwith each of a plurality of workers; receiving a proposed personnelaction related to a worker; applying a data mining tool to theattributes and the proposed personnel action to identify an impact ofthe proposed personnel action; and outputting the impact of the proposedpersonnel action based on the result produced by the data mining tool.2. The computer-readable medium according to claim 1, wherein the impactcomprises an impact on a performance of the worker.
 3. Thecomputer-readable medium according to claim 1, wherein the impactcomprises an impact on a performance of peers of the worker.
 4. Thecomputer-readable medium according to claim 1, wherein the impactcomprises an impact on a likelihood of attrition of the worker.
 5. Thecomputer-readable medium according to claim 1, wherein the proposedpersonnel action comprises a salary increase.
 6. The computer-readablemedium according to claim 1, wherein the proposed personnel actioncomprises a promotion.
 7. The computer-readable medium according toclaim 1, wherein the proposed personnel action comprises a transfer. 8.The computer-readable medium according to claim 1, wherein theoutputting comprises outputting the result to a graph.
 9. Acomputer-implemented method, comprising: collecting a plurality ofattributes associated with each of a plurality of workers; receiving aproposed personnel action related to a worker; applying a data miningtool to the attributes and the proposed personnel action to identify animpact of the proposed personnel action; and outputting the impact ofthe proposed personnel action based on the result produced by the datamining tool.
 10. The method according to claim 9, wherein the impactcomprises an impact on a performance of the worker.
 11. The methodaccording to claim 9, wherein the impact comprises an impact on aperformance of peers of the worker.
 12. The method according to claim 9,wherein the impact comprises an impact on a likelihood of attrition ofthe worker.
 13. The method according to claim 9, wherein the outputtingcomprises outputting the result to a graph.
 14. An apparatus,comprising: memory configured to store a plurality of attributesassociated with each of a plurality of workers; a processor configuredto receive a proposed personnel action related to a worker; apply a datamining tool to the attributes and the proposed personnel action toidentify an impact of the proposed personnel action; and output theimpact of the proposed personnel action based on the result produced bythe data mining tool.
 15. The apparatus according to claim 14, whereinthe impact comprises an impact on a performance of the worker.
 16. Theapparatus according to claim 14, wherein the impact comprises an impacton a performance of peers of the worker.
 17. The apparatus according toclaim 14, wherein the impact comprises an impact on a likelihood ofattrition of the worker.
 18. The apparatus according to claim 14,wherein the processor is configured to output the result to a graph.